The British Accounting Review - diva-portal.org987620/FULLTEXT01.pdf · ABC measures the cost and performance of activities, resources, and cost objects (Player & Cobble 1999: 247),

The British Accounting Review xxx (2013) 1–14

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The British Accounting Review

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Assessing the interaction effect of cost control systemsand information technology integration on manufacturingplant financial performance

Adam S. Maiga a, Anders Nilsson b,*, Fred A. Jacobs c

a School of Accounting, Florida International University, Miami, USAbDepartment of Business Administration, Technology and Social Sciences, Accounting and Control, Luleå University of Technology,Luleå, SwedencDepartment of Business, Economics and Law, Sundsvall, Sweden

Keywords:IT integrationCost control systemsManufacturing plant financial performanceActivity-based costingVolume-based costing

* Corresponding author.E-mail addresses: [email protected] (A.S. Maiga), l

0890-8389/$ – see front matter � 2013 Elsevier Ltdhttp://dx.doi.org/10.1016/j.bar.2013.10.001

Please cite this article in press as: Maiga,technology integration on manufacturing10.1016/j.bar.2013.10.001

a b s t r a c t

The interface between management control and information technology is an under-developed research area with a knowledge gap concerning its implications for financialperformance. This study contributes to bridging this gap by investigating the interactioneffect of cost control systems and information technology integration on manufacturingplant financial performance. We surveyed a sample of 518 managers of U.S. manufacturingplants, approximately evenly distributed between those using activity-based costing andvolume-based costing. Using hierarchical regression analyses, results indicate that whileinformation technology integration and cost control systems do not provide significantindependent effects on plant financial performance, they do interact to positively impactmanufacturing plant financial performance. Thus, our findings suggest that manufacturingplants will reap the greatest financial performance benefits from investments in activity-based cost control systems when combined with information technology integration.

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1. Introduction

Successful IT integration can deliver IT resources in support of the new roles and functions ofworkers as a result of redesignedand tightenedbusinessprocesses (Rockart, Ear,&Ross,1996). Fromabroadsocial andorganizationalpointofview, a companywitha high level of IT integration across different channels of operation may be able to transmit, combine, and process external datafrom customers and suppliers/vendors. It may also be effortless in such a company to share data among various internal systems(e.g., forecasting, production, shipment, and accounting) and to retrieve information fromvarious databases for decision support(e.g., cost information, reporting tools). Further, external and internal systems can automatically reflect order changes in down-stream processes or systems (e.g., inventory andmanufacturing systems) (Barua, Konana,Whinston, & Yin, 2004; Sikora & Shaw,1998) and help monitor order status at various stages in the process of a manufacturing plant (e.g., manufacturing, shipment).

The research on the organizational financial performance impact of information technology (IT) has been referred to as ITbusiness value research (Kohli & Grover, 2008; Melville, Kraemer, & Gurbaxani, 2004; Mukhopadhyay, Kekre, & Kalathur,1995). Prior studies in this area suggest that organizations should realize greater financial performance benefits whensuch resources are increasingly integrated (Simoens & Scott, 2005; Weiner, Savitz, Bernard, & Pucci, 2004). However, the ITliterature reveals mixed empirical results with respect to organizational financial performance achieved from IT integration(Bharadwaj, 2000; Chapman & Kihn, 2009; Hunton, Lippincott, & Reck, 2003; Poston & Grabski, 2001). For example,

[email protected] (A. Nilsson), [email protected] (F.A. Jacobs).

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A.S. Maiga et al. / The British Accounting Review xxx (2013) 1–142

Bharadwaj (2000) compared the financial performance of firms that had been recognized by InformationWeekmagazine as ITleaders in their industry to the financial performance of a control group not having such recognition. She found that firmswith high IT capabilities (firms in the study sample) outperformed firms from the control group. Hayes, Hunton, and Reck(2001) found that capital markets place value on enterprise resource planning (ERP) implementations, but Poston andGrabski (2001) found that ERP implementations have no effect on firm financial performance. Following these results, theresearch questions turn from whether investments in IT have a positive impact on financial performance to a question ofwhen and why there is a financial performance effect (Dehning & Richardson, 2002).

A parallel development in the literature has been to increasingly attend to contemporary management accounting de-velopments, including “new” management accounting information such as activity-based costing (ABC) (e.g., Abdel-Maksoud, Dugdale, & Luther, 2005; Banker, Bardhan, & Chen, 2008). Accounting systems require formalized categories forcollecting and reporting information, and create a common language with which members of the organization cancommunicate (Wouters & Verdaasdonk, 2002). This facilitates the coordination between different functions that need toprovide input to the decision-making processes (Galbraith, 1973).

Despitemany assertions offinancial benefits resulting fromABC systems, the empirical results have beenmixed (Bromwich& Bhimani, 1989; Gordon & Silvester, 1999; Innes & Mitchell, 1995; Ittner, Lanen, & Larcker, 2002; Rafiq & Garg, 2002). Forexample, while empirical findings by Rafiq and Garg (2002) suggest that there is a strong relationship between ABC imple-mentationandprofitability,manyother studiesfindno relationshipbetweenABCandprofitability (Bromwich&Bhimani,1989;Gordon & Silvester, 1999; Innes & Mitchell 1995; Ittner et al., 2002; Maiga & Jacobs, 2008). Additionally, several reservationshave been expressed regarding the efficacy of ABC (Innes, Mitchell, & Sinclair, 2000; Malmi, 1997; Morrow & Connolly, 1994).

There has been conjecture regarding why the effect of IT integration or cost control systems, such as ABC, has not beenconsistently shown to have a positive impact on organizational financial performance. Indeed, some studies claim thatfinancial performance-enhancing programs have been implemented inways that lack balancewith competing priorities, thusresulting in a reduction of, or no increase in, financial performance (Shields, 1995). For example, Milgrom (1992) suggests thatthe value that a resource can bring to an organization might remain limited unless other complementary factors are adoptedand implemented as well. This is in line with Milgrom and Roberts’ (1995) framework which suggests that factors in a systemof mutually enhancing elements will operate in such away that doingmore of any of these factors increases the attractivenessof doing more of the other factors in the system. These arguments suggest that successful outcomes of management ac-counting systems may be contingent upon the implementation of new manufacturing techniques.

While the literature does recognize the importance of contingency variables, there is little cross-fertilization between thestream of literature which highlights contemporary developments in cost control systems and the literature on organization-level IT integration (Drake & Haka, 2008). Although the intense development of information technology over recent yearsopens new venues for modeling and integrating organizational activities (Berry, Coad, Harris, Otley, & Stringer, 2009), anddespite suggestions of potentially important synergies between IT and accounting, their complementarity effects onmanufacturing plant financial performance have not been empirically assessed. As such, the interface of management controland information technology is still an under-developed area both empirically and theoretically (Granlund &Mouritsen, 2003).This gap in accounting literature constitutes the motivation for this paper. Therefore, the first contribution of this study is toattempt to develop a workable framework that integrates existing theory and research in management accounting and in-formation technology. The second contribution is to develop a theory-driven hypothesis and to attempt to bridge this gapbetweenmanagement accounting and IT byevaluating the possible interaction effect of cost control systems and IT integrationonmanufacturing plant financial performance. In so doing, we are responsive toTopkis (1995) who suggests that investmentsin information and production technologies cannot stimulate productivity and growth without a number of complementaritydevelopments. To our knowledge no operationalizations of this complementarity have been presented so far, making thisstudy interesting for continued research in this area. Based on these contributions, this study provides knowledge that adds tothe research which seeks to integrate the information technology and management accounting literatures.

Using hierarchical regression analyses, our results indicate that while information technology integration and cost controlsystems do not provide significant independent effects on plant financial performance, they do interact to positively impactmanufacturing plant financial performance. This finding implies that maximizing the performance benefits of cost controlsystems and information technology integration require attending to the integration between them, rather than treating thelevels of cost control systems and information technology integration as independent decisions. The study thereby subscribesto, and provides statistically significant evidence in support of, the conclusion of Dechow, Richardson, and Sloan (2005: 691)who suggest that “.control cannot be studied apart from technology”.

This paper is organized as follows. In the next section, the theoretical background and hypothesis development arepresented. These topics are followed by the discussion of the research methods. Subsequent sections address the results,conclusions, and limitations of the study.

2. Theoretical background and hypothesis development

2.1. Cost control systems

ABC measures the cost and performance of activities, resources, and cost objects (Player & Cobble 1999: 247), and eval-uates whether those activities add value, thus providing a means of understanding how to most effectively reduce costs. ABC

Please cite this article in press as: Maiga, A. S., et al., Assessing the interaction effect of cost control systems and informationtechnology integration on manufacturing plant financial performance, The British Accounting Review (2013), http://dx.doi.org/10.1016/j.bar.2013.10.001

A.S. Maiga et al. / The British Accounting Review xxx (2013) 1–14 3

was promoted as a method for reducing inaccuracies experienced with traditional costing systems that arise from prevalenttechnology and competition (Dodd & Lavelle, 2002). Proponents argue that ABC is a more refined cost system type thatprovides greater detail, better classifies costs according to behavior, reports cost information more frequently, can providemore accurate cost data, and results in the ability to calculate more variances (Pizzini, 2006). ABC may allow for “better” (i.e.,more relevant and useful) data that enhance managerial decision-making, enabling improved performance (e.g., Cooper &Kaplan, 1991a; Ittner et al., 2002; Johnson, 1992; Krumwiede, 1998).

Khanna (2002) argues that the primary failings of traditional costing systems are the inability to provide useful feedbackor understand and allocate overhead costs. Traditional systems also have the potential inability to account for the size anddiversity of products, as a larger or more complex item that may produce more revenue, may also consume a larger thanpresumed overhead cost (Doyle, 2002). Brewer, Brownlee, and Juras (2003) argue that these issues can have a negative effecton a company’s financial performance.

Despite the fact that the ultimate aim of ABC is promoted as the improvement of financial performance (Cooper &Kaplan, 1991a), several reservations have been expressed regarding the efficacy of ABC (Innes et al., 2000; Malmi, 1997;Morrow & Connolly, 1994). The arguments in support of ABC are based on the presumed comparative advantage thatfirms may derive from greater transparency and accuracy of information obtained from ABC (Banker et al., 2008; Cagwin &Bouwman, 2002). However, Kaplan (1993) and others have caution that ABC implementation may not produce directbenefits. Indeed, the role of other facilitators and contextual factors, such as implementation of related organizationalinitiatives, has gained greater importance in this debate (Anderson, Hesford, & Young, 2002; Banker et al., 2008; Chenhall,2003; Henri, 2006).

2.2. IT integration

Prior research provides awell-agreed definition of information system integration, such as ERP, as “enterprise wide packagesthat tightly integrate business functions into a single systemwith a shared database” (Chapman & Kihn, 2009; Lee & Lee, 2000;Newell, Huang, Galliers, & Pan, 2003; Quattrone & Hopper, 2005). When integrated, the various information systems create anenvironment that provides a degree of interoperability which stand-alone “componentized” systems fail to achieve. In addition,integrated information technologynotonlyenablesprocessautomation, but also canprovide theability todisseminate timelyandaccurate information, resulting in improved managerial and employee decision-making (Hitt, Wu, & Zhou, 2002).

Although integrated information technology is generally designed and introduced by non-accountants, it is closely con-nected with the accounting processes (Chapman, 2005). Information technology plays a critical role in modern business,especially regarding the accounting function (Efendi, Mulig, & Smith, 2006: 117), and management control (Dechow et al.,2005). IT has radically transformed the nature of business and accounting practice (Hunton, 2002). As indicated bySadagopan (2003), some of the most ordinary accounting processes which are incorporated in an information technologysystem include: general ledger, accounts receivable, accounts payable, financial control, asset management, funds flow, costcenters, profit centers, profitability analysis, order and project accounting, product cost accounting, and performance analysis.Accordingly, information technology integration systems should have implications for all areas of accounting (Hunton, 2002;Sutton, 2006). The rationale for deployment of such systems generally is reductions in the routine tasks of managementaccountants (Arnold & Sutton, 2007; Drury, 2008; Lowe, 2004) and the subsequent transition in their role from a transaction-oriented information gatherer, to a business oriented information analyst (Granlund & Lukka, 1998; Hunton, 2002), or moresimply from the back office to the front office (Holtzman, 2004).

2.2.1. Interaction effect of cost control systems and IT integrationInformation technologies, suchasERPsystems,arepackagesof computerapplications that supportmany, evenmost, aspectsof

a company’s information needs (Davenport, 2000: 2). However, the idea that information systems are able to deliver support formanagement accounting is not new (Rom & Rohde, 2007). IT represents a precious assistance in the search for and treatment ofinformation needed in decision-making processes (O’Connor & Martinsons, 2006). Thus, management accounting practices,whether traditional ormodern,maybecomemoreefficientandeffectivewhensupportedwith integrated information technology(e.g., Baxendale & Jama, 2003; Edwards, 2001; He, 2007; Lea, 2007; Lea & Min, 2003; Scapens & Jazayeri, 2003).

Integrated information technology, such as ERP systems, has been suggested to lead to the standardization of datacollection formats and reporting, and to new opportunities for adapting accounting information to the needs that emerge inlocal decision situations (Granlund & Malmi, 2002). IT can provide organizational members with quick and effective access tothe right amounts of information (Hope & Hope, 1997). If connectivity is enhanced through IT, firm members can more easilyshare individual interpretations of the cost information, thereby making consensus development more efficient.

Prior research (e.g., Cooper & Kaplan 1991b; Estrin, Kantor, & Albers, 1994; Pattison & Arendt 1994) has identified specificenvironmental conditions that affect the potential benefits from the use of ABC. The prior research supports the propositionthat under appropriate conditions more detailed costing information provided by ABC leads to improved decision-making,enabling improved performance. For example, Baxendale and Jama (2003) suggest that in an ABC environment, the avail-ability and reliability of activity cost driver information becomes more beneficial when supported by integrated informationtechnology. Furthermore, Kudyba and Vitaliano (2003) suggest that the enhanced flow of information brought about by ITintegration and cost control systems throughout the company empowers decision makers to streamline operations byreducing unnecessary waste, such as idle factors of production (labor and capital), and increasing profitability.


ITIIT Integration

Cost ControlSystems

RFinancial Performance

Control VariablesPlantageSizeDiscreteDiversityEarnbonusCompetition

Fig. 1. Conceptual model.


Based on the above arguments, we contend that while a cost control system, such as ABC, provides the content and structureof data collection and reporting, and while an integrated information technology supplies the information processing andconnectivity tools, it follows that neither cost control systems nor integrated information technology may have a direct sig-nificant impact on plant financial performance when used in isolation. Rather, cost control systems may interact with infor-mation technology integration to improve plant financial performance. This argument is in line with the contingency theory ofmanagement accounting which suggests that organizational performance requires fit between the use of management ac-counting systems and contextual variables (Baines & Langfield-Smith, 2003; Chenhall, 2003; Chenhall &Morris,1986; Haldma &Laats, 2002; Hoque, 2004; Hyvönen, 2007; Morton & Hu, 2008; Otley, 1980). Thus, we hypothesize (Fig. 1):

1 The2 Bec

Pleastechn10.10

H1: The two-way interaction between cost control systems and IT integration is positively related to manufacturingplant financial performance.

3. Research design and methods

3.1. Sample

The initial sample of 2506U.S.manufacturing plants (bothABC andnon-ABC plants) used in this studywasfirst used byMaigaand Jacobs (2008) in their study of the extent of ABC use and its consequences. To address the hypotheses, survey questionnaireswere sent to the director of manufacturing, manager, and/or the chief operating officer of the manufacturing plant. The ques-tionnaires1 were pre-coded to enable non-respondents to be identified for a second mailing and later pairing. A self-addressed,postage-paid envelope was attached for returning the completed questionnaire directly to one of the researchers.

The survey cover letter described the objectives of the study and promised anonymity. An explanation of the controlnumber also appeared on the survey cover letter. Within the first three weeks, 478 plants responded. To increase the responserate, we sent follow-up letters and another copy of the questionnaire to those who had not responded. This second mailingresulted in 89 responding plants. The following criteria were used for inclusion of the responses in the data analysis: (1) eachplant should be an investment center, (2) at least two complete responses should have been received from each plant, and (3)each manager should have held the current position for at least two years with the plant. A total of 20 plants was eliminatedbecause they did not meet criterion 2 (questionnaires were received from only one respondent at the plant). Additionally, 29responses were excluded for incompleteness.2 This process resulted in 518 usable responses (272 for the ABC group and 246for the non-ABC group), a 20.67% response rate. Table 1 provides a more detailed analysis of the responses received.

Previous studies have indicated a varying ABC implementation rate in the range of some 20–30%. For example Ittner et al.(2002) found a rate of 26%, Maiga and Jacobs (2008) noted a rate of 27.6% and Banker et al. (2008) had a 19.8% implementationrate in their sample. After data collection, we could determine that our sample indicated a rather high ABC implementationrate of 52.5%. Having used identical items and the same two-digit standard classification (SIC) as Banker et al. (2008) andIttner et al. (2002) we decided to explore the reason for our implementation rate by means of a comparison with our sampleand that of Banker et al. (2008) as the most recent of those studies. Results of this analysis, along with secondary data on U.S.manufacturers from Banker et al. (2008), are displayed in Table 2 below.

This comparison revealed that our sample was composed in such away that each of the sectors food and kindred products,chemical and allied products, rubber and plastics products, and electronics and electrical equipment contributed more than10% to the total sample. The implementation rate for each of these sectors turned out to be high, spanning from 44.8 to 82.7%.This characteristic of our sample differentiates it from the sample in Banker et al. (2008) where these industries are repre-sented to a much lower extent, and it also sets our sample apart from the percentage of U.S. manufacturers in the sectorsconcerned. If, as in this case, a large weight in the total sample is attributed to industry sectors with a high implementation

complete questionnaire is available from the corresponding author upon request.ause of contravening company policy, some preferred not to participate.

e cite this article in press as: Maiga, A. S., et al., Assessing the interaction effect of cost control systems and informationology integration on manufacturing plant financial performance, The British Accounting Review (2013), http://dx.doi.org/16/j.bar.2013.10.001

Table 1Summary of responses received.

ABC Non-ABC (VBC) Total

First wave 255 223 478Second wave 41 48 89Total 296 271 567Plants eliminated from the study because they did not meet the required 2 or more response criteria 11 9 20Plants eliminated because of incomplete responses 13 16 29Plants with usable responses 272 246 518


rate this will also increase the implementation rate of the sample as a whole. The comparison also revealed differencesbetween the studies in terms of which specific SIC codes, in the 20–40 interval, that had been used. There is thus a distinctpossibility that our implementation rate has been affected by the sample composition.

Non-response bias is always a concern in survey research. We tested for non-response bias in two ways. First, we per-formed t-tests to determine if the sample was different from the general population of manufacturing plants used in thisstudy. Data were obtained from the Annual Survey of Manufacturers conducted in the United States. This survey estimates alimited amount of industry-level data based on an annual survey. From this source, we were able to extract return on assets(ROA) and plant size as measured by the number of employees. We compared the ABC responding sample and non-ABCpopulation that did not respond using the two measures. We also compared the total responding group (both ABC andnon-ABC) with the general population. In either case, neither ROA nor size was significantly different from the numbersprovided by the Annual Survey of Manufacturers. Second, we tested for statistical differences in the responses between theearly and latewaves of survey respondents (early ABC responds vs. late ABC respondents; and total early respondents vs. totallate respondents) with the last wave of surveys received considered representative of non-respondents (Armstrong &Overton, 1977). t-tests were performed to compare the mean scores of the early and late responses. There were no statisti-cally significant differences between the early and late respondents, providing some assurance concerning non-response bias.

Next, we calculated inter-respondent reliability using a Spearman–Brown interclass correlation coefficient (Shrout & Fleiss,1979). The results indicated that inter-respondent reliability was high across all questions in the survey (ranging from 0.76 to0.83). Therefore, we averaged the responses from each plant to arrive at a single representation of each variable value per plant.

3.2. Measurement and validation of variables

The variables used to test the hypotheses are cost control system, IT integration, financial performance, and the followingcontrol variables: Plant Age, Size, Discrete, Diversity, Competition, and Earnbonus. The Appendix contains an abbreviatedcopy of the research questionnaire used to measure the self-reported variables.

3.2.1. Cost control systemsWe followed Banker et al. (2008) for inquiry designed to identify ABC and non-ABC [volume-based costing (VBC)] adopting

plants. The ABC adoption variable was defined based on the response to the survey question asking whether ABC wasimplemented at the plant (0 ¼ not implemented, 1 ¼ plan to implement, 2 ¼ extensively implemented). For the purpose ofour study, we collapsed the first two categories into one category, which represents plants that had not implemented ABC atthe time of the survey. As in prior studies (e.g., Anderson, 1995; Gosselin, 1997; Ittner et al., 2002; Krumwiede, 1998), wefocused on extensive use of ABC data rather than a continuum of ABC adoption levels to avoid problems with plants that arejust beginning to implement ABC or that have not achieved full commitment to the systems. Hence, wemeasure ABC as a 0–1dummy variable where zero represents “no implementation” and one represents “extensive implementation”.

3.2.2. IT integrationFor the purpose of this study, we measured this variable based on prior studies (e.g., Barua et al., 2004; Chapman & Kihn,

2009). The construct was measured on a seven-point Likert scale (1¼ never, 7¼ all the time) with the following inquiries: (1)“Our plant’s information systems allow continuous monitoring of order status at various stages in the process (e.g.,manufacturing, shipping).” (2) “Data can be shared easily among various internal systems (e.g., forecasting, production,manufacturing, shipment, finance, accounting, etc.).” (3) “Order changes are automatically reflected in downstream processesor systems (e.g., inventory, manufacturing resource planning, and manufacturing systems).” (4) “Employees are able toretrieve information from various databases for decision support (e.g., cost information, reporting tools).” (5) “Our systemscan easily transmit, integrate, and process data from suppliers and customers.”We used a seven-point Likert scale to increasethe sensitivity of the measurement instrument.3 Factor analysis of the scale produced one component with an eigenvalue of79.371% of the total variation. A reliability check of the instrument for the study produced a Cronbach (1951) alpha of 0.883.Thus, the IT integration score was calculated as the average of the manager’s score for the five items in the instrument.

3 The use of a seven-point scale is believed to be appropriate as it is the most common scale in U.S. research (Wolak, Kalafatis, & Harris, 1998).


Table 2Excerpt from sample distribution by industry in comparison to Banker et al. (2008).

Industry sector SICcode

Percent of sample(Banker et al., 2008)

Percent of U.S.manufacturers

Percent of samplein present study

Implementation ratein present study

Food and kindred products 20 3.76% 5.76% 10.425% 57.4%Chemicals and allied products 28 6.88 3.41 11.197 44.8Rubber and plastics 30 5.92 0.52 10.618 58.2Electronics and electrical

equipment36 13.44 4.71 10.039 82.7


3.2.3. Financial performanceBased on prior studies (Kinney & Wempe 2002; Maiga & Jacobs 2008), we measured financial performance by asking

respondents to indicate the extent to which their plants have experienced improvement in (1) “Return on sales (net incomebefore corporate expenses divided by sales)”, (2) “Turnover on assets (sales divided by total assets)”, and (3) “Return on assets(net income before corporate taxes divided by total assets)”. The construct was measured on a seven-point Likert scale(1 ¼ much worse than our competitors, 7 ¼ much better than our competitors).

Factor analysis of the scale produced one component with an eigenvalue of 80.710% of the total variation. A reliabilitycheck of the instrument for the study produced a Cronbach (1951) alpha of 0.854. Thus, the financial performance wasmeasured as the average response of the manager’s score for the three items in the instrument.

3.2.4. Control variablesSome of the cross-sectional variations in plant profitability can be explained only if controls are appropriately applied.

Hence, we controlled for the impact of plant characteristics on manufacturing performance following prior studies (Bankeret al., 2008; Bjørnenak, 1997; Brown, Booth, & Giacobbe, 2004; Ittner et al., 2002; Maiga & Jacobs, 2008). We included thefollowing control variables in our tests:

Plant age (PLANTAGE) is likely to play a significant role since older plants often fail to realize the impact of technology-enabled processes on manufacturing performance (Banker et al., 2008). Plant age represented the number of yearssince plant start-up to the time of the study (Banker et al., 2008).Plant size (SIZE) is likely to impact manufacturing performance since smaller plants are likely to be more agile inresponding to customer needs compared to larger plants, ceteris paribus (Banker et al., 2008; Hendricks & Singhal, 1997).Plant size was measured as the number of plant employees (Banker et al., 2008).Discrete product environment (DISCRETE) indicated operations where the primary products were measured in numericquantities, while process manufacturing indicated whether the primary products were measured by weight or volume. Adiscrete production environment was expected to lead to higher manufacturing performance (Bardhan, Whitaker, &Mithas, 2006; Ittner et al., 2002). DISCRETE ¼ 1 if the nature of manufacturing operations for primary products wasdiscrete, 0 otherwise (Banker et al., 2008).Product diversity (DIVERSITY) occurs when plants offer a varied set of products – possibly at a higher pricedto sell moregoods and to further segment the market. As a result there is a trade-off between efficiency, cost or time reduction, anddiversity (Lancaster, 1979). The accounting and operations management literatures have emphasized the potential costsfrom increasing variety (e.g., Cooper & Kaplan, 1991a; Hayes &Wheelwright, 1984; Miller & Vollman, 1985; Skinner, 1974).Similarly we expected that product diversity would negatively impact manufacturing performance. DIVERSITY wasmeasured as 1 if products were diverse, 0 otherwise.Earnings-based bonus (EARNBONUS) is a variable included to control for the possibility that earnings-based bonus plansfor management result in the achievement of higher manufacturing performance. That is, it is possible that plants thatprovide earnings-based bonus plans are more likely to improve their performance (Banker, Potter, & Srinivasan, 2000;Bruns & McKinnon, 1993; Coates, Davis, Emmanuel, Longden, & Stacey, 1992). Earnbonus equaled 1 if the plant had anearnings-based bonus plan, 0 otherwise (Kinney & Wempe, 2002; Maiga & Jacobs, 2008).Market competition (COMPETITION), according to economic theory, enhances incentives for raising performance. Evidenceof the positive role of market competition is given in studies by Dutz and Hayri (1999) and Nickell (1996). However, La Portaand Lopenz-de-Silanes (1999) failed to find performance improvement with more intense competition. Market competitionwasmeasured on a seven-point Likert scale (1¼ low competition, 4¼medium competition, 7¼ high competition) by askingrespondents to indicate “the extent to which their plants have experienced market competition over the last five years”.

3.3. Data analysis techniques

In empirical contingency research, hierarchical regression analysis is used to establish the existence of a statisticallysignificant interaction effect (e.g., Arnold & Evans, 1979; Cohen & Cohen,1983; Cronbach,1987; Southwood,1978). In additionto running a regression with only the control variables [cf. Equation (1)], we ran two regressions, one with the control var-iables and themain effects [cf. Equation (2)] and another with the control variables, the main effects, and the interaction term


Table 3Respondents’ characteristics.

Panel A: Industry Classification

SIC Total Percentage ABC Percentage VBC Percentage

Food and kindred products 20 54 10.425% 31 11.397% 23 9.350Textile mill products 22 25 4.826 17 6.250 8 3.252Apparel and other textile products 23 28 5.405 17 6.250 11 4.472Paper and allied products 26 51 9.846 27 9.926 24 9.756Chemicals and allied products 28 58 11.197 26 9.559 32 13.008Rubber and plastics products 30 55 10.618 32 11.765 23 9.350Primary metal industries 33 67 12.934 21 7.721 46 18.699Fabricated metal products 34 37 7.143 12 4.412 25 10.163Industrial machinery and equipment 35 42 8.108 21 7.721 21 8.537Electronic, electrical equipment 36 52 10.039 43 15.809 9 3.659Transportation equipment 37 33 6.371 16 5.882 17 6.911Instruments and related products 38 16 3.089 9 3.309 7 2.846TOTAL 518 100% 272 100% 246 100%

Panel B: Other Characteristics of Respondents

Minimum Maximum Mean Standard deviation

Length at present position (years) 2 9 7.23 1.06Length in management (years) 4 19 14.26 3.17


[cf. Equation (3)]. A significant interaction effect is confirmed by the statistical significance of the additional varianceexplained by the inclusion of the interaction term (i.e., the significance of the increase in R2). This method is equivalent to thesimpler and more direct assessment of the significance of the t-value associated with the coefficient of the product term (seeArnold, 1982: 157; Jaccard, Turrisi, & Wan, 1990: 22; Southwood, 1978: 1168). Since the main effects of the two-way inter-action model are arbitrary and have no theoretical meaning as we have used non-ratio scales (Allison, 1977; Schoonhoven,1981; Southwood, 1978) the hierarchical approach also permits the main effects and the interactive effects of the indepen-dent variables to be analyzed separately. Following these approaches, the following regression models were employed:

F� Performance ¼ a0 þ a1Plantageþ a2Sizeþ a3Discreteþ a4Diversityþ a5Earnbonusþ a6Competitionþ z (1)

F�Performance¼g0þg1Plantageþg2Sizeþg3Discreteþg4Diversityþg5Earnbonusþg6Competitionþg7ITIþg8CCSþε

(2)

F� Performance ¼ b0 þ b1Plantageþ b2Sizeþ b3Discreteþ b4Diversityþ b5Earnbonusþ b6Competitionþ b7ITI

þ b8CCSþ b9ðITI� CCSÞ þ d

(3)whereF-Performance¼manufacturingplantfinancialperformanceasmeasuredbytheaverageof three response items, i.e., returnon sales (ROS), turnover on assets (TOA), and return on assets (ROA). Plantage ¼ plant age, Size ¼ plant size, Discrete ¼ discreteproduction environment, Diversity ¼ product diversity, Earnbonus¼ earnings-based bonus, and Competition ¼ market compe-tition. CCS ¼ cost control systems operationalized as a dummy variable with 0 ¼ volume-based costing and 1 ¼ activity-basedcosting. ITI ¼ information technology integration. ITI � CCS ¼ interaction term. z, ε and d are the error terms.

We make three assumptions when interpreting the estimation results of the models. First, we assume that some firmshave not chosen their IT integration and cost control systems optimally, so that manufacturing plant financial performancewill vary cross-sectionally with the observed IT integration and cost control system choices. Second, we assume that ourvariables have lowmeasurement error and the functional form of themodels is appropriate. Finally, we assume IT integration,cost control systems, and the control variables are exogenous, making the coefficient estimates for our models consistent.

4. Results

In this section, we first present the descriptive statistics. Then we examine the hypotheses and conduct further analyses.

4.1. Descriptive statistics

The descriptive statistics in Table 3, Panel A, provide the profile of the responding companies, showing that they constitutea broad spectrum of manufacturers as defined by the two-digit SIC code. The sample composition has representation inprimary metals (12.934%), followed by chemicals and allied products (11.197%), rubber and plastics (10.618%), food andkindred products (10.425%), and electronic, electrical equipment (10.039%). Additional information on respondents’ char-acteristics is provided in Table 3, Panel B. Answers to the question regarding number of years with the manufacturing plantshowed that the respondents have a mean of 7.23 years in their current position. To the number of years in management


Table 4Correlations, means and standard deviations for the variables (N ¼ 518).

1. 2. 3. 4. 5. 6. 7. 8. 9.

1. Plantage 12. Size �0.014 13. Discrete 0.792** 0.008 14. Diversity 0.020 0.476** 0.011 15. Earnbonus 0.780** �0.013 0.861** �0.002 16. Competition 0.020** 0.500** 0.017 0.735* �0.017 17. CCS 0.095* 0.031 0.094* 0.018 0.075 0.025 18. ITI 0.093** 0.101* 0.114** 0.083 0.071 0.133** 0.159** 19. F-Performance 0.011 0.231** �0.028 0.485** �0.011 0.455** 0.061 0.0271Mean 3.392 3.284 0.419 0.367 0.417 4.176 0.525 4.214 4.091Std. Dev. 0.504 0.716 0.494 0.482 0.494 1.590 0.500 1.399 1.610

*Significant at 0.05 level (two-tailed), **significant at the 0.01 level (two-tailed).Note: Plantage ¼ plant age, Discrete ¼ discrete production environment, Diversity ¼ product diversity, Earnbonus ¼ earning-based bonus,Competition ¼ market competition, CCS ¼ cost control systems, ITI ¼ information technology integration, F-Performance ¼ financial performance.


question, respondents indicated a mean of 14.26 years. It appears from their positions and tenure that the respondents areknowledgeable and experienced, have access to information upon which to provide reliable perceptions, and are otherwisewell qualified to provide the information required.4

The Pearson correlation matrix as well as means and standard deviations for the variables included in the model is shownin Table 4. It is notable that the lack of significant correlation between product diversity and cost control systems can beconsidered surprising, given that product diversity is often presented as a motive for implementing sophisticated costingsystems (e.g., Cooper, 1988; Estrin et al., 1994). However, a case study by Abernethy, Lillis, Brownell, and Carter (2001) on theimplications of product diversity of cost systems design in Australia indicated that the design of cost systems was notinfluenced by product diversity. Also, using a sample of UK companies at business unit level, Al-Omiri and Drury (2007) foundno association between the level of cost system sophistication and cost structure and product diversity. Similarly, Bjørnenak(1997) and Krumwiede (1998) failed to link cost systems with product diversity.

4.2. Regression results

Before presenting the regression results, we first discuss the issue of multicollinearity. Dewar and Werbel (1979) arguedthat hierarchical models like Equation (2) suffer from multicollinearity since the cross-product term is likely to be stronglycorrelated with the terms that compose it. However, several researchers (Gupta & Govindarajan, 1993; Smith & Sasaki, 1979;Southwood, 1978) have demonstrated that such multicollinearity can be completely eliminated by manipulating the originpoints for the continuous variables. Therefore, in this study, the continuous variable (IT integration) is mean centered beforeentering to avoid nonessential multicollinearity. Such a transformation in the origin points does not in any way affect thevalue or the significance of the regression coefficients. Hence, two statistical measures of multicollinearity are the tolerance(TOL) value and the variance inflation factor (VIF) (Hair, Anderson, Tatham, & Black, 1998). A low tolerance value indicates ahigh degree of collinearity. The VIF and TOL measures assume normality and are typically relative measure. A high tolerancevalue (above 0.10) and lowVIF value (below 10) usually suggest a relatively small degree of multicollinearity (Hair et al., 1998).In the regression in Table 5, the largest variance inflation factor was 4.585 and the lowest tolerance value 0.218, suggesting nobias in the standard errors of regression coefficients among the variables used in the models.

The standardized regression results are reported in Table 4. Equation (1) shows the results for the control variables. NeitherPlantage nor Size has a significant impact on plant financial performance (a ¼ 0.027, p ¼ 0.647; a ¼ �0.053, p ¼ 0.190,respectively), the coefficient for Discrete is significant, but negative (a ¼ �0.249, p ¼ 0.001). However, Diversity, Earnbonus,and Competition have significant effect on financial performance [Diversity (a ¼ 0.291, p ¼ 0.000), Earnbonus (a ¼ 0.201,p ¼ 0.005), and Competition (a ¼ 0.405, p ¼ 0.000)].

In Equation (2), results indicate the main effects of cost control systems and IT integration. Neither variable is significantlyrelated to financial performance (g¼ 0.051, p¼ 0.145, and g¼�0.014, p¼ 0.691, respectively). However, as shown in Equation(3), interacting variables (cost control systems � information technology integration) significantly associated with financialperformance (b ¼ 0.237, p ¼ 0.000). In addition, the explained variance (R2) was 42.5%, hence increasing 2.6% from the maineffects model (Equation (2)), with significant F-value (41.749, p ¼ 0.000).5 Therefore, H1 was supported. The results indicatethat while information technology integration and cost control systems do not provide significant independent effects onplant financial performance, they do interact to positively impact manufacturing plant financial performance.6

4 The range of ABC adoption length varies from 5 to 19 years.5 For a two-way interaction equation (i.e., Equation (3)), all main effects should be included, and it should be noted that the coefficients obtained for

these main effects are not directly interpretable (Cohen & Cohen 1983: 348; Govindarajan & Fisher, 1990). Therefore, the focus is on the two-way interactioncoefficient.

6 We also regressed each financial performance measure (ROS, TOA, ROA) on the independent variables. Results indicate that, similar to the results inTable 4, the coefficient of the interaction term (CCS � ITI) is significant and positive for each dependent variable (ROS, TOA, ROA).


Table 5Results of regression for financial performance.

Equation (1) Equation (2) Equation (3)

a p-value b p-value b p-value

Plantage 0.027 0.647 0.024 0.683 0.023 0.698Size �0.053 0.19 �0.054 0.185 �0.075 0.06Discrete �0.249 0.001 �0.25 0.001 �0.227 0.002Diversity 0.291 0 0.29 0 0.302 0Earnbonus 0.201 0.005 0.202 0.005 0.198 0.005Competitive 0.405 0 0.407 0 0.427 0CCS 0.051 0.145 0.052 0.131ITI �0.014 0.691 �0.188 0CCS � ITI 0.237 0R2 0.396 0.399 0.425Adj. R2 0.389 0.39 0.425DR2 – 0.003 0.026F(p-value) 55.948(0.000) 42.246(0.000) 41.749(0.000)

Note: Plantage ¼ plant age, Discrete ¼ discrete production environment, Diversity ¼ product diversity, Earnbonus ¼ earning-based bonus,Competition ¼ market competition, CCS ¼ cost control systems, ITI ¼ information technology integration.


5. Discussion, conclusions, limitations and subsequent research

This study assesses the interaction effect of cost control systems and IT integration on manufacturing plant financialperformance. The results of the study support the theoretical arguments. While the main effects of IT integration and costcontrol systems on plant financial performance are not significant, their interaction effect indicates a significant positiveeffect on plant financial performance. As suggested by Ashby’s (1958) law of requisite variety, the greater variety in costinformation resulting from cost control systempracticesmay require amatching level of variety in information flows, enabledby a high level of IT integration.

On the basis of the findings of this survey, the results of this study show that the cost control system/IT integrationinterface provides a plausible explanation of conflicting results in prior studies assessing the relationship between ABCor information technology on financial performance in isolation. The results are consistent with theoretical argumentspresented in the information systems literature (e.g., Sambamurthy, Bharadwaj, & Grover, 2003), and extend pastfindings in information systems integration (Bharadwaj, 2000). This study also extends past management accountingresearch that advocates systemic approaches to examining the effectiveness of management accounting systems(e.g., Chenhall, 2003; Chenhall & Langfield-Smith, 1998), and its results help extend the boundaries of cost controlsystems.

The findings of this study have both theoretical and practical implications. A theoretical implication of the results is thenecessity of addressing performance effects of cost control systems jointly with information technology. The focus on theinteraction effect of cost control systems and IT integration on manufacturing plant financial performance represents aninteresting and relatively unexplored area in management accounting research. At the level of practice, the findings providepractical guidance to managers involved in resource allocation decisions. This study’s findings recommend that in terms ofcost control systems, such as ABC, IT integration should be adopted as it is interacts with cost control systems to significantlyimpact manufacturing plant financial performance.

Limitations of this study should bementioned. First, ABC implementationwasmeasured as a 0–1 variable in our study. It ispossible that using a more granular scale tomeasure the extent of ABC implementation, including the level of ABC integrationand the time lag since ABC implementation (Banker et al., 2008). For example, it is possible to identify empirically theappropriate definition(s) for the adoption and non-adoption of ABC by testing the homogeneity of various definitions of ABCadoption and non-adoption across the level of competition, product customization, manufacturing overhead cost percentageand size of operating units (Brierley, 2011). This may provide greater insights on the interaction effect of cost control systemsand information technology integration on plant financial performance.

Second, the scales employed in this study represent individuals’ perceptions of the variables used and, consequently, theymay not reflect objective reality. Future studies can extend this study by utilizing different methodologies, such as casestudies, and adopting a longitudinal approach, which would strengthen the underlying theory suggested by this study. Third,this study examines manufacturing performance strictly in financial terms. Other dimensions of performance, includingquality, flexibility, and manufacturing cycle time and lead time, should be taken into consideration in future studies (see, forexample, Ittner et al., 2002). Fourth, as the sample was derived from U.S. manufacturing plants, the results of this study canonly be generalized to this sector of the economy. Opportunities exist to extend this study to other industries and othercountries. Also, further research is necessary to validate the present results.

Despite the limitations above, this study provides additional evidence regarding the complex issue of IT and ABC andperformance.


Appendix

Survey questions

Please answer the questionnaire (or pass it to the most appropriate person within the plant and return it to the address mentioned in the information in this survey may be sensitive, but we assure you that the data will only be used

in the aggregate in the study, and the answers in this questionnaire will be treated in the strictest confidence and no information gained any particular person or manufacturing plant.

cover letter. We recognize that some of the

from this survey will be identified with






References

Abdel-Maksoud, A., Dugdale, D., & Luther, R. (2005). Non-financial performance measurement in manufacturing companies. The British Accounting Review,37, 261–297.

Abernethy, M. A., Lillis, A. M., Brownell, P., & Carter, P. (2001). Product diversity and costing system design choice: field study evidence. Management Ac-counting Research, 12(3), 261–279.

Al-Omiri, M., & Drury, C. (2007). A survey of factors influencing the choice of product costing systems in UK organizations.Management Accounting Research,18, 399–424.

Allison, P. D. (1977). Testing for interaction in multiple regression. American Journal of Sociology, 83, 144–153.Anderson, S. W. (1995). A framework for assessing cost management system changes: the case of activity-based costing implementation at General Motors,

1986–1993. Journal of Management Accounting Research, 7, 1–51.Anderson, S. W., Hesford, J., & Young, S. M. (2002). Factors influencing the performance of activity-based costing teams: a field study of ABC model

development time in the automobile industry. Accounting, Organizations and Society, 27, 195–211.Armstrong, J. S., & Overton, T. (1977). Estimating non-response bias in mail surveys. Journal of Marketing Research, 14, 396–402.Arnold, H. J. (1982). Moderator variables: a clarification of conceptual, analytic and psychometric issues. Organizational Behavior and Human Performance, 29,

143–174.Arnold, H. J., & Evans, M. G. (1979). Testing multiplicative models does not require ratio scales. Organizational Behavior and Human Performance, 24, 41–59.Arnold, V., & Sutton, S. G. (2007). The impact of enterprise systems on business and audit practice and the implications for university accounting education.

International Journal of Enterprise Information Systems, 3(4), 1–21.Ashby, W. R. (1958). Requisite variety and its implications for the control of complex systems. Cybernetica, 1(2), 83–99.Baines, A., & Langfield-Smith, K. (2003). Antecedents to management accounting change: a structural equation approach. Accounting, Organizations and

Society, 28(7,8), 675–698.Banker, R. D., Bardhan, I. R., & Chen, T. Y. (2008). The role of manufacturing practices in mediating the impact of activity-based costing on plant performance.

Accounting, Organizations and Society, 33, 1–19.Banker, R. D., Potter, G., & Srinivasan, D. (2000). An empirical investigation of an incentive plan that includes non-financial performance measures. The

Accounting Review, 75(1), 65–92.Bardhan, I. R., Whitaker, J., & Mithas, S. (2006). Information technology, production process outsourcing and manufacturing plant performance. Journal of

Management Information Systems, 23(2), 13–40.Barua, A., Konana, P., Whinston, A. B., & Yin, F. (2004). Assessing Internet enabled business value: an exploratory investigation. MIS Quarterly, 28(4),

585–620.Baxendale, S. J., & Jama, F. (2003). What ERP can offer ABC. Strategic Finance, 85(2), 54–57.Berry, A. J., Coad, A. F., Harris, E. P., Otley, D. T., & Stringer, C. (2009). Emerging themes in management control: a review of recent literature. The British

Accounting Review, 41, 2–20.Bharadwaj, A. S. (2000). Resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Quarterly,

24(1), 169–196.Bjørnenak, T. (1997). Diffusion and accounting: the case of ABC in Norway. Management Accounting Research, 8(1), 3–17.Brewer, P., Brownlee, R., II, & Juras, P. (2003). Global Electronics, Inc.: ABC implementation and the change management process. Issues in Accounting

Education, 18(1), 49–69.Brierley, J. A. (2011). Why the proper definition of the ABC matters: a note. Advances in Management Accounting, 19, 225–249.Bromwich, M., & Bhimani, A. (1989). Management accounting: Evolution, not revolution. London: Chartered Institute of Management Accountants.Brown, D. A., Booth, P., & Giacobbe, F. (2004). Technological and organizational influences on the adoption of activity-based costing in Australia. Accounting

and Finance, 44, 329–356.Bruns, W. J., Jr., & McKinnon, S. M. (1993). Information and managers: a field study. Journal of Management Accounting Research, 5(84), 35–46.Cagwin, D., & Bouwman, M. J. (2002). The association between activity-based costing and improvement in financial performance. Management Accounting

Research, 13, 1–39.Chapman, C. S. (2005). Not because they are new: developing the contribution of enterprise resource planning systems to management control research.

Accounting, Organizations and Society, 30, 685–689.Chapman, C. S., & Kihn, L. A. (2009). Information systems integration, enabling control and performance. Accounting Organizations and Society, 34, 151–169.Chenhall, R. (2003). Management control systems design within its organizational context: findings from contingency-based research and directions for the

future. Accounting, Organizations and Society, 28, 127–168.Chenhall, R. H., & Langfield-Smith, K. (1998). Adoption and benefits of management accounting practices: an Australian study. Management Accounting

Research, 9, 1–19.Chenhall, R., & Morris, D. (1986). The impact of structure, environment and interdependencies on the perceived usefulness of management accounting

systems. The Accounting Review, 61, 16–35.Coates, J. B., Davis, E. W., Emmanuel, C. R., Longden, S. G., & Stacey, R. J. (1992). Multinational companies performance measurement systems: international

perspectives. Management Accounting Research, 3, 133–150.Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale: Erlbaum.Cooper, R. (1988). The rise of activity-based costing-part three: how many cost drivers do you need, and how do you select them? Journal of Cost Man-

agement34–46. Manufacturing Industry.Cooper, R., & Kaplan, R. S. (1991a). Profit priorities from activity-based costing. Harvard Business Review, 130–135.Cooper, R., & Kaplan, R. S. (1991b). The design of cost management systems (1st ed.). Englewood Cliffs: Prentice Hall.Cronbach, L. J. (1951). Coefficient alpha and the internal consistency of tests. Psychometrika, 16, 297–334.Cronbach, L. J. (1987). Statistical tests for moderator variables: flaws in analyses recently proposed. Psychological Bulletin, 102, 414–417.Davenport, T. H. (2000). Mission critical. Realizing the promise of enterprise systems. Boston, MA: Harvard Business School Press.Dechow, P. M., Richardson, S. A., & Sloan, R. G. (2005). The persistence and pricing of the cash component of earnings. University of Michigan. Working paper.Dehning, B., & Richardson, D. J. (2002). Returns of investments in information technology: a research synthesis. Journal of Information Systems, 16(1), 7–30.Dewar, R., & Werbel, J. (1979). Universalistic and contingency predictions of employee satisfaction and conflict. Administrative Science Quarterly, 24,

426–448.Dodd, D., & Lavelle, W. (2002). ABC spells improved performance. High Volume Printing, 20(6), 20–29.Doyle, S. (2002). Software review: is there a role for activity-based costing (ABC) in database marketing. Journal of Database Marketing, 10, 175–180.Drake, A. R., & Haka, S. F. (2008). Does ABC information exacerbate hold-up problems in buyer-supplier negotiations? The Accounting Review, 83(1), 29–60.Drury, C. (2008). Management and cost accounting (7th ed.). London: Gengage Learning.Dutz, M., & Hayri, A. (1999). Does more intense competition lead to higher growth? CEPR. Discussion Paper No. 2249.Edwards, J. B. (2001). ERP, balanced scorecard, and IT: how do they fit together? Journal of Corporate Accounting & Finance, 12(5), 3–12.Efendi, J., Mulig, E., & Smith, L. (2006). Information technology and systems research published in major accounting academic and professional journals.

Journal of Emerging Technologies in Accounting, 3, 117–128.Estrin, T. L., Kantor, J., & Albers, D. (1994). Is ABC suitable for your company? Management Accounting, 75, 40–45.Galbraith, J. (1973). Designing complex organizations. Reading, MA: Addison-Wesley.Gordon, L. A., & Silvester, K. J. (1999). Stock market reactions to activity-based costing adoption. Journal of Accounting and Public Policy, 18(3), 229–235.


http://refhub.elsevier.com/S0890-8389(13)00082-6/sref1














































































Gosselin, M. (1997). The effect of strategy and organizational structure on the adoption and implementation of activity based costing. Accounting, Orga-nizations and Society, 22(2), 105–122.

Govindarajan, V., & Fisher, J. (1990). Strategy, control systems, and resource sharing: effects on business unit performance. Academy of Management Journal,33(2), 259–285.

Granlund, M., & Lukka, K. (1998). It’s a small world of management accounting practices. Journal of Management Accounting Research, 10, 153–179.Granlund, M., & Malmi, T. (2002). Moderate impact of ERPS on management accounting: a lag or permanent outcome? Management Accounting Research,

13(3), 299–321.Granlund, M., & Mouritsen, J. (2003). Special section on management control and new information technologies. European Accounting Review, 12(1),

77–83.Gupta, A. K., & Govindarajan, V. (1993). Coalignment between knowledge flow patterns and the strategic systems and processeswithinMNCs. In P. Lorange, B.

Chakravarthy, J. Roos, & A. Van de Ven (Eds.), Implementing strategic processes: Change, learning and cooperation (pp. 329–346). London: Basil Blackwell.Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. New Jersey: Prentice-Hall.Haldma, T., & Laats, K. (2002). Contingencies influencing the management accounting practices of Estonian manufacturing companies. Management Ac-

counting Research, 13(4), 379–400.Hayes, R., & Wheelwright, S. (1984). Restoring our competitive edge. New York: John Wiley & Sons.Hayes, D. C., Hunton, J. E., & Reck, J. L. (2001). Market reaction to ERP implementation announcements. Journal of Information Systems, 15, 3–18.He, Y. (2007). A research on the integration between ERP system and ABCM. In Proceedings of the 2nd international conference on research and practical issues

of enterprise information systems (pp. 781–786). Beijing, China.Hendricks, K. B., & Singhal, V. R. (1997). Does implementing an effective TQM program actually improve operating performance? Empirical evidence from

firms that have won quality awards. Management Science, 44, 1258–1274.Henri, J. F. (2006). Management controls systems and strategy: a resource-based perspective. Accounting, Organizations and Society, 31, 529–558.Hitt, L., Wu, D., & Zhou, X. (2002). Investment in enterprise resource planning: business impact and productivity measures. Journal of Management In-

formation Systems, 19(1), 71–98.Holtzman, Y. (2004). The transformation of the accounting profession in the United States: from information processing to strategic business advising.

Journal of Management Development, 23(10), 949–961.Hope, J., & Hope, T. (1997). Competing in the third wave: The ten management issues of the information age. Boston: Harvard Business School Press.Hoque, Z. (2004). A contingency model of the association between strategy, environmental uncertainty and performance measurement: impact on

organizational performance. International Business Review, 13(4), 485–502.Hunton, J. (2002). Blending information and communication technology with accounting research. Accounting Horizons, 16(1), 55–67.Hunton, J. E., Lippincott, B., & Reck, J. L. (2003). Enterprise resource planning (ERP) systems: comparing firm performance of adopters and non-adopters.

International Journal of Accounting Information Systems, 4(3), 165–184.Hyvönen, J. (2007). Strategy, performance measurement techniques and information technology of the firm and their links to organizational performance.

Management Accounting Research, 18(3), 343–366.Innes, J., & Mitchell, F. (1995). A survey of activity-based costing in the U.K.’s largest companies. Management Accounting Research, 6(2), 137–149.Innes, J., Mitchell, F., & Sinclair, D. (2000). Activity-based costing in the U.K.’s largest companies: a comparison of 1994 and 1999 survey results.Management

Accounting Research, 11(3), 349–362.Ittner, C. D., Lanen, W. N., & Larcker, D. F. (2002). The association between activity-based costing and manufacturing performance. Journal of Accounting

Research, 40(3), 711–726.Jaccard, J., Turrisi, R., & Wan, C. K. (1990). Interaction effects in multiple regression. Newbury Park: Sage.Johnson, H. T. (1992). Relevance regained: From top-down control to bottom-up empowerment. New York: The Free Press.Kaplan, R. (1993). Research opportunities in management accounting. Journal of Management Accounting Research, 5, 1–14.Khanna, V. (2002). Learn the ABC of business. Businessline, 1.Kinney, M. R., & Wempe, W. F. (2002). Further evidence on the extent and origins of JIT’s profitability effects. The Accounting Review, 77(1), 203–225.Kohli, R., & Grover, V. (2008). Business value of IT: an essay on expanding research directions to keep up with the times. Journal of the Association for

Information Systems, 9(1), 23–39.Krumwiede, K. R. (1998). ABC: why it’s tried and how it succeeds. Management Accounting, 79, 32–36.Kudyba, S., & Vitaliano, D. (2003). Information technology and corporate profitability: a focus on operating efficiency. Information Resources Management

Journal, 16(1), 1–13.La Porta, R., & Lopenz-de-Silanes, F. (1999). The benefits of privatization: evidence from Mexico. Quarterly Journal of Economics, 114, 1193–1242.Lancaster, T. (1979). Econometric methods for the duration of unemployment. Econometrica, 47, 939–956.Lea, B. R. (2007). Management accounting in ERP integrated MRP and TOC environments. Industrial Management & Data Systems, 107(8), 1188–1211.Lea, B. R., & Min, H. (2003). Selection of management accounting systems in just-in-time and theory of constraints-based manufacturing. International

Journal of Production Research, 41(13), 2879–2910.Lee, Z., & Lee, J. (2000). An ERP implementation case study from a knowledge transfer perspective. Journal of Information Technology, 15, 281–288.Lowe, A. (2004). Postsocial relations: toward a performative view of accounting knowledge. Accounting, Auditing & Accountability Journal, 17(4), 604–628.Maiga, A. S., & Jacobs, F. A. (2008). Extent of ABC use and its consequences. Contemporary Accounting Research, 25(2), 533–566.Malmi, T. (1997). Towards explaining activity-based costing failure: accounting and control in a decentralized organization. Management Accounting

Research, 8(4), 459–470.Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Review: information technology and organizational performance: an integrative model of IT business

value. MIS Quarterly, 28(2), 283–322.Milgrom, P. (1992). Economics, organization and management. Prentice-Hall.Milgrom, P., & Roberts, J. (1995). Complementarities and fit: strategy, structure, and organizational change in manufacturing. Journal of Accounting and

Economics, 19, 179–208.Miller, J. G., & Vollman, T. E. (1985). The hidden factory. Harvard Business Review, 142–150.Morrow, M., & Connolly, T. (1994). Practical problems of implementing ABC. Accountancy, 5(3), 76–78.Morton, N. A., & Hu, Q. (2008). Implications of the fit between organizational structure and ERP: a structural contingency theory perspective. International

Journal of Information Management, 28(5), 391–402.Mukhopadhyay, T., Kekre, S., & Kalathur, S. (1995). Business value of information technology: a study of electronic data interchange. MIS Quarterly, 19(2),

137–156.Newell, S., Huang, J., Galliers, R., & Pan, S. (2003). Implementing enterprise resource planning and knowledge management systems in tandem: fostering

efficiency and innovation incomplementarity. Information and Organization, 13(1), 25–52.Nickell, S. (1996). Competition and corporate performance. Journal of Political Economy, 104, 724–746.O’Connor, N. G., & Martinsons, M. G. (2006). Management of information systems: insights from accounting research. Information & Management, 43(8),

1014–1024.Otley, D. (1980). The contingency theory of management accounting: achievement and prognosis. Accounting, Organizations and Society, 5, 413–428.Pattison, D., & Arendt, C. (1994). Activity-based costing: it doesn’t work all the time. Management Accounting, LXXV, 61–66.Pizzini, M. J. (2006). The relation between cost-system design, managers’ evaluations of the relevance and usefulness of cost data, and financial perfor-

mance: an empirical study of US hospitals. Accounting, Organizations and Society, 31, 179–210.Player, S., & Cobble, C. (1999). Cornerstones of decision making: Profiles of enterprise ABM. Oakhill Press.
















































































Poston, R., & Grabski, S. (2001). Financial impact of enterprise resource planning implementations. International Journal of Accounting Information Systems,2(4), 271–294.

Quattrone, P., & Hopper, T. (2005). A ‘Time-space odyssey’: management control systems in multinational organizations. Accounting, Organizations andSociety, 30(7–8), 735–764.

Rafiq, A., & Garg, A. (2002). Activity based costing and financial institutions: old wine in new bottles or corporate panacea? Journal of Bank Cost andManagement Accounting, 15(2), 12–28.

Rockart, J. F., Ear, M. J., & Ross, J. W. (1996). Eight imperatives for the new IT organization. Sloan Management Review, 38(1), 43–56.Rom, A., & Rohde, C. (2007). Management accounting and integrated information systems: a literature review. International Journal of Accounting Infor-

mation Systems, 8, 40–68.Sadagopan, S. (2003). Enterprise resource planning (pp. 169–184). Elsevier Science.Sambamurthy, V., Bharadwaj, A., & Grover, V. (2003). Shaping agility through digital options: reconceptualizing the role of information technology in

contemporary firms. MIS Quarterly, 2(27), 237–263.Scapens, R. W., & Jazayeri, M. (2003). ERP systems and management accounting change: opportunities or impacts? A research note. European Accounting

Review, 12(1), 201–233.Schoonhoven, C. B. (1981). Problems with contingency theory: testing assumptions hidden within the language of contingency theory. Administrative

Science Quarterly, 26, 349–377.Shields, M. D. (1995). An empirical analysis of firms’ implementation experiences with activity-based costing. Journal of Management Accounting Research, 7,

148–166.Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing reliability. Psychological Bulletin, 86, 420–428.Sikora, R., & Shaw, M. (1998). A multi-agent framework for the coordination and integration of information systems. Management Science, 44(11), 65–78.Simoens, S., & Scott, A. (2005). Integrated primary care organizations: to what extent is integration occurring and why. Health Services Management

Research, 18(1), 25–40.Skinner, W. (1974). The focused factory. Harvard Business Review, 52(3), 113–121.Smith, K. W., & Sasaki, M. S. (1979). Decreasing multicollinearity: a method for models with multiplicative functions. Sociological Methods and Research, 8,

35–56.Southwood, T. R. E. (1978). Ecological methods. New York: Wiley.Sutton, S. G. (2006). Extended-enterprise systems’ impact on enterprise risk management. Journal of Enterprise Information Management, 19(1/2), 97–114.Topkis, D. (1995). Comparative statics of the firm. Journal of Economic Theory, 67, 370–401.Weiner, B. J., Savitz, L. A., Bernard, S., & Pucci, L. G. (2004). How do integrated delivery systems adopt and implement clinical information systems? Health

Care Management Review, 29(1), 1–16.Wolak, R., Kalafatis, S., & Harris, P. (1998). An investigation into four characteristics of services. Journal of Empirical Generalizations in Marketing Science, 3,

22–41.Wouters, M., & Verdaasdonk, P. (2002). Supporting management decisions with ex ante accounting information. European Management Journal, 20(1),

82–94.




































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The British Accounting Review - diva-portal.org987620/FULLTEXT01.pdf · ABC measures the cost and performance of activities, resources, and cost objects (Player & Cobble 1999: 247),